Reassessing the Prognostic Value of Lymph Node Metastasis in Deficient Mismatch Repair Colorectal Cancer
Bibliographic record
Abstract
Background: In non-metastatic deficient mismatch repair (dMMR) colorectal cancer (CRC), traditional prognostic factors, such as pN staging, often fail to distinguish patient outcomes effectively. Methods: This retrospective study included a cohort of 792 dMMR CRC patients who underwent surgical treatment without neoadjuvant chemoradiotherapy or immunotherapy. Traditional prognostic factors were compared with lymph node-based models (NLN, LNR, LOODS) for their ability to predict overall survival (OS) and disease-free survival (DFS). Results: The study demonstrated that traditional factors, such as histologic type, differentiation, and vascular invasion, had limited predictive value in dMMR CRC. Furthermore, the pN stage failed to effectively distinguish between pN1 and pN2 for both OS (p = 0.219) and DFS (p = 0.095). Conversely, LOODS demonstrated superior performance over traditional pN staging in predicting both OS and DFS (p < 0.001). A prognostic model combining LOODS with age exhibited superior predictive performance compared with the traditional TN staging system. Conclusions: LOODS was identified as a more effective independent prognostic factor compared with traditional pN staging, enabling more precise stratification of pN+ patients in non-metastatic dMMR CRC, highlighting its potential utility in guiding postoperative treatment and optimizing therapeutic strategies.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".